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Contextual word embedding models such as ELMo (Peters et al., 2018) and BERT (Devlin et al., 2018) have dramatically improved performance for many natural language processing (NLP) tasks in recent months.
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
Jinhyuk Lee, Wonjin Yoon, Sungdong Kim, Donghyeon Kim, Sunkyu Kim, Chan Ho So, and Jaewoo Kang. 2019 · 1901
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ScispaCy: Fast and Robust Models for Biomedical Natural Language Processing
Mark Neumann, Daniel King, Iz Beltagy, and Waleed Ammar. 2019 · 1902
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Enhancing Clinical Concept Extraction with Contextual Embedding
Yuqi Si, Jingqi Wang, Hua Xu, and Kirk Roberts. 2019 · 1902
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Text Chunking using Transformation-Based Learning
Lance A. Ramshaw and Mitchell P. Marcus. 1995 · 1995
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Evaluating the state-of-the-art in automatic de-identification
Ozlem Uzuner, Yuan Luo, and Peter Szolovits. 2007 · 2007
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2010 i2b2/VA challenge on concepts, assertions, and relations in clinical text
Özlem Uzuner, Brett R South, Shuying Shen, and Scott L DuVall. 2011 · 2010
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Evaluating temporal relations in clinical text: 2012 i2b2 Challenge
Weiyi Sun, Anna Rumshisky, and Ozlem Uzuner. 2013b · 2012
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Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. 2013 · 2013
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Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher Manning. 2014 · 2014
Cited alongside, same era.
SemEval-2014 Task 7: Analysis of Clinical Text
Sameer Pradhan, Noémie Elhadad, Wendy Chapman, Suresh Manandhar, and Guergana Savova. 2014 · 2014
Cited alongside, same era.
Automated systems for the de-identification of longitudinal clinical narratives: Overview of 2014 i2b2/UTHealth shared task Track 1
Amber Stubbs, Christopher Kotfila, and Özlem Uzuner. 2015 · 2014
Cited alongside, same era.
Annotating longitudinal clinical narratives for de-identification: The 2014 i2b2/UTHealth corpus
Amber Stubbs and Özlem Uzuner. 2015 · 2014
Cited alongside, same era.
SemEval-2015 Task 14: Analysis of Clinical Text
Noemie Elhadad, Sameer Pradhan, Sharon Gorman, Suresh Manandhar, Wendy Chapman, and Guergana Savova · 2015
Cited alongside, same era.
MIMIC-III, a freely accessible critical care database
Supervised Learning of Universal Sentence Representations from Natural Language Inference Data
Alexis Conneau, Douwe Kiela, Holger Schwenk, Loic Barrault, and Antoine Bordes. 2017 · 2017
Later among the works it cites.
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
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Universal Language Model Fine-tuning for Text Classification
Jeremy Howard and Sebastian Ruder. 2018 · 2018
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A Deep Learning Architecture for De-identification of Patient Notes: Implementation and Evaluation
Kaung Khin, Philipp Burckhardt, and Rema Padman. 2018 · 2018
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Alistair E. W. Johnson, Tom J. Pollard, Lu Shen, Li-wei H. Lehman, Mengling Feng, Mohammad Ghassemi, Benjamin Moody, Peter Szolovits, Leo Anthony Celi, and Roger G. Mark. 2016 · 2016
Cited alongside, same era.
Enriching word vectors with subword information
Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov. 2017 · 2017
Cited alongside, same era.
Annotating temporal information in clinical narratives
Weiyi Sun, Anna Rumshisky, and Ozlem Uzuner. 2013a
Cited in the paper.
Matthew E. Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018 · 2018
Later among the works it cites.
Lessons from Natural Language Inference in the Clinical Domain
Alexey Romanov and Chaitanya Shivade. 2018 · 2018
Later among the works it cites.
Clinical Concept Extraction with Contextual Word Embedding
Henghui Zhu, Ioannis Ch Paschalidis, and Amir Tahmasebi. 2018 · 2018
Later among the works it cites.